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<a href="#pub-types">Public 类型</a> &#124;
<a href="#pub-methods">Public 成员函数</a> &#124;
<a href="#pro-methods">Protected 成员函数</a> &#124;
<a href="#pri-methods">Private 成员函数</a> &#124;
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<div class="title">pcl::SIFTKeypoint&lt; PointInT, PointOutT &gt; 模板类 参考</div>  </div>
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<p><b><a class="el" href="classpcl_1_1_s_i_f_t_keypoint.html" title="SIFTKeypoint detects the Scale Invariant Feature Transform keypoints for a given point cloud dataset ...">SIFTKeypoint</a></b> detects the Scale Invariant <a class="el" href="classpcl_1_1_feature.html" title="Feature represents the base feature class. Some generic 3D operations that are applicable to all feat...">Feature</a> Transform keypoints for a given point cloud dataset containing points and intensity. This implementation adapts the original algorithm from images to point clouds.  
 <a href="classpcl_1_1_s_i_f_t_keypoint.html#details">更多...</a></p>

<p><code>#include &lt;<a class="el" href="sift__keypoint_8h_source.html">sift_keypoint.h</a>&gt;</code></p>
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类 pcl::SIFTKeypoint&lt; PointInT, PointOutT &gt; 继承关系图:</div>
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  <img src="classpcl_1_1_s_i_f_t_keypoint.png" usemap="#pcl::SIFTKeypoint_3C_20PointInT_2C_20PointOutT_20_3E_map" alt=""/>
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<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-types"></a>
Public 类型</h2></td></tr>
<tr class="memitem:a3c8b9828c9449a549e679aca5c924640"><td class="memItemLeft" align="right" valign="top"><a id="a3c8b9828c9449a549e679aca5c924640"></a>
typedef boost::shared_ptr&lt; <a class="el" href="classpcl_1_1_s_i_f_t_keypoint.html">SIFTKeypoint</a>&lt; PointInT, PointOutT &gt; &gt;&#160;</td><td class="memItemRight" valign="bottom"><b>Ptr</b></td></tr>
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typedef boost::shared_ptr&lt; const <a class="el" href="classpcl_1_1_s_i_f_t_keypoint.html">SIFTKeypoint</a>&lt; PointInT, PointOutT &gt; &gt;&#160;</td><td class="memItemRight" valign="bottom"><b>ConstPtr</b></td></tr>
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typedef <a class="el" href="classpcl_1_1_keypoint.html">Keypoint</a>&lt; PointInT, PointOutT &gt;::<a class="el" href="classpcl_1_1_point_cloud.html">PointCloudIn</a>&#160;</td><td class="memItemRight" valign="bottom"><b>PointCloudIn</b></td></tr>
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typedef <a class="el" href="classpcl_1_1_keypoint.html">Keypoint</a>&lt; PointInT, PointOutT &gt;::<a class="el" href="classpcl_1_1_point_cloud.html">PointCloudOut</a>&#160;</td><td class="memItemRight" valign="bottom"><b>PointCloudOut</b></td></tr>
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typedef <a class="el" href="classpcl_1_1_keypoint.html">Keypoint</a>&lt; PointInT, PointOutT &gt;::<a class="el" href="classpcl_1_1search_1_1_search.html">KdTree</a>&#160;</td><td class="memItemRight" valign="bottom"><b>KdTree</b></td></tr>
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<tr class="memitem:ad0fa140c6038e3708a7ef652e5c74caa inherit pub_types_classpcl_1_1_keypoint"><td class="memItemLeft" align="right" valign="top"><a id="ad0fa140c6038e3708a7ef652e5c74caa"></a>
typedef boost::shared_ptr&lt; <a class="el" href="classpcl_1_1_keypoint.html">Keypoint</a>&lt; PointInT, PointOutT &gt; &gt;&#160;</td><td class="memItemRight" valign="bottom"><b>Ptr</b></td></tr>
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typedef boost::shared_ptr&lt; const <a class="el" href="classpcl_1_1_keypoint.html">Keypoint</a>&lt; PointInT, PointOutT &gt; &gt;&#160;</td><td class="memItemRight" valign="bottom"><b>ConstPtr</b></td></tr>
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typedef <a class="el" href="classpcl_1_1_p_c_l_base.html">PCLBase</a>&lt; PointInT &gt;&#160;</td><td class="memItemRight" valign="bottom"><b>BaseClass</b></td></tr>
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typedef <a class="el" href="classpcl_1_1search_1_1_search.html">pcl::search::Search</a>&lt; PointInT &gt;&#160;</td><td class="memItemRight" valign="bottom"><b>KdTree</b></td></tr>
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typedef <a class="el" href="classpcl_1_1search_1_1_search.html">pcl::search::Search</a>&lt; PointInT &gt;::Ptr&#160;</td><td class="memItemRight" valign="bottom"><b>KdTreePtr</b></td></tr>
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typedef <a class="el" href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a>&lt; PointInT &gt;&#160;</td><td class="memItemRight" valign="bottom"><b>PointCloudIn</b></td></tr>
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typedef PointCloudIn::Ptr&#160;</td><td class="memItemRight" valign="bottom"><b>PointCloudInPtr</b></td></tr>
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typedef PointCloudIn::ConstPtr&#160;</td><td class="memItemRight" valign="bottom"><b>PointCloudInConstPtr</b></td></tr>
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typedef <a class="el" href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a>&lt; PointOutT &gt;&#160;</td><td class="memItemRight" valign="bottom"><b>PointCloudOut</b></td></tr>
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typedef boost::function&lt; int(int, double, std::vector&lt; int &gt; &amp;, std::vector&lt; float &gt; &amp;)&gt;&#160;</td><td class="memItemRight" valign="bottom"><b>SearchMethod</b></td></tr>
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typedef boost::function&lt; int(const <a class="el" href="classpcl_1_1_point_cloud.html">PointCloudIn</a> &amp;cloud, int index, double, std::vector&lt; int &gt; &amp;, std::vector&lt; float &gt; &amp;)&gt;&#160;</td><td class="memItemRight" valign="bottom"><b>SearchMethodSurface</b></td></tr>
<tr class="separator:ac58660587c72beaf0068c4f4656484e6 inherit pub_types_classpcl_1_1_keypoint"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="inherit_header pub_types_classpcl_1_1_p_c_l_base"><td colspan="2" onclick="javascript:toggleInherit('pub_types_classpcl_1_1_p_c_l_base')"><img src="closed.png" alt="-"/>&#160;Public 类型 继承自 <a class="el" href="classpcl_1_1_p_c_l_base.html">pcl::PCLBase&lt; PointInT &gt;</a></td></tr>
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typedef <a class="el" href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a>&lt; PointInT &gt;&#160;</td><td class="memItemRight" valign="bottom"><b>PointCloud</b></td></tr>
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typedef PointCloud::Ptr&#160;</td><td class="memItemRight" valign="bottom"><b>PointCloudPtr</b></td></tr>
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typedef PointCloud::ConstPtr&#160;</td><td class="memItemRight" valign="bottom"><b>PointCloudConstPtr</b></td></tr>
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typedef boost::shared_ptr&lt; <a class="el" href="structpcl_1_1_point_indices.html">PointIndices</a> &gt;&#160;</td><td class="memItemRight" valign="bottom"><b>PointIndicesPtr</b></td></tr>
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typedef boost::shared_ptr&lt; <a class="el" href="structpcl_1_1_point_indices.html">PointIndices</a> const &gt;&#160;</td><td class="memItemRight" valign="bottom"><b>PointIndicesConstPtr</b></td></tr>
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</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-methods"></a>
Public 成员函数</h2></td></tr>
<tr class="memitem:a8873bdacc195701f40e7145a2cf23b96"><td class="memItemLeft" align="right" valign="top"><a id="a8873bdacc195701f40e7145a2cf23b96"></a>
&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_s_i_f_t_keypoint.html#a8873bdacc195701f40e7145a2cf23b96">SIFTKeypoint</a> ()</td></tr>
<tr class="memdesc:a8873bdacc195701f40e7145a2cf23b96"><td class="mdescLeft">&#160;</td><td class="mdescRight">Empty constructor. <br /></td></tr>
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<tr class="memitem:aa7b165d782eca9c9d226504b84729439"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_s_i_f_t_keypoint.html#aa7b165d782eca9c9d226504b84729439">setScales</a> (float min_scale, int nr_octaves, int nr_scales_per_octave)</td></tr>
<tr class="memdesc:aa7b165d782eca9c9d226504b84729439"><td class="mdescLeft">&#160;</td><td class="mdescRight">Specify the range of scales over which to search for keypoints  <a href="classpcl_1_1_s_i_f_t_keypoint.html#aa7b165d782eca9c9d226504b84729439">更多...</a><br /></td></tr>
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<tr class="memitem:a5fcabfa4b90cc77cf0c18a5b263d06a0"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_s_i_f_t_keypoint.html#a5fcabfa4b90cc77cf0c18a5b263d06a0">setMinimumContrast</a> (float min_contrast)</td></tr>
<tr class="memdesc:a5fcabfa4b90cc77cf0c18a5b263d06a0"><td class="mdescLeft">&#160;</td><td class="mdescRight">Provide a threshold to limit detection of keypoints without sufficient contrast  <a href="classpcl_1_1_s_i_f_t_keypoint.html#a5fcabfa4b90cc77cf0c18a5b263d06a0">更多...</a><br /></td></tr>
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<tr class="memitem:ab968a8e4e03374f001f2ad32f71c19f6 inherit pub_methods_classpcl_1_1_keypoint"><td class="memItemLeft" align="right" valign="top"><a id="ab968a8e4e03374f001f2ad32f71c19f6"></a>
void&#160;</td><td class="memItemRight" valign="bottom"><b>harrisCorner</b> (ImageType &amp;output, ImageType &amp;input, const float sigma_d, const float sigma_i, const float alpha, const float thresh)</td></tr>
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void&#160;</td><td class="memItemRight" valign="bottom"><b>hessianBlob</b> (ImageType &amp;output, ImageType &amp;input, const float sigma, bool SCALE)</td></tr>
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void&#160;</td><td class="memItemRight" valign="bottom"><b>hessianBlob</b> (ImageType &amp;output, ImageType &amp;input, const float start_scale, const float scaling_factor, const int num_scales)</td></tr>
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void&#160;</td><td class="memItemRight" valign="bottom"><b>imageElementMultiply</b> (ImageType &amp;output, ImageType &amp;input1, ImageType &amp;input2)</td></tr>
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<tr class="memitem:a06d5bf39feac19b1d5dfa0a3e87caea9 inherit pub_methods_classpcl_1_1_keypoint"><td class="memItemLeft" align="right" valign="top"><a id="a06d5bf39feac19b1d5dfa0a3e87caea9"></a>
&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_keypoint.html#a06d5bf39feac19b1d5dfa0a3e87caea9">Keypoint</a> ()</td></tr>
<tr class="memdesc:a06d5bf39feac19b1d5dfa0a3e87caea9 inherit pub_methods_classpcl_1_1_keypoint"><td class="mdescLeft">&#160;</td><td class="mdescRight">Empty constructor. <br /></td></tr>
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<tr class="memitem:abc3023e139444291b4d155553369b0bd inherit pub_methods_classpcl_1_1_keypoint"><td class="memItemLeft" align="right" valign="top"><a id="abc3023e139444291b4d155553369b0bd"></a>
virtual&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_keypoint.html#abc3023e139444291b4d155553369b0bd">~Keypoint</a> ()</td></tr>
<tr class="memdesc:abc3023e139444291b4d155553369b0bd inherit pub_methods_classpcl_1_1_keypoint"><td class="mdescLeft">&#160;</td><td class="mdescRight">Empty destructor <br /></td></tr>
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<tr class="memitem:a9dbce2da2ea97dcb4f5635df33a129c9 inherit pub_methods_classpcl_1_1_keypoint"><td class="memItemLeft" align="right" valign="top">virtual void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_keypoint.html#a9dbce2da2ea97dcb4f5635df33a129c9">setSearchSurface</a> (const PointCloudInConstPtr &amp;cloud)</td></tr>
<tr class="memdesc:a9dbce2da2ea97dcb4f5635df33a129c9 inherit pub_methods_classpcl_1_1_keypoint"><td class="mdescLeft">&#160;</td><td class="mdescRight">Provide a pointer to the input dataset that we need to estimate features at every point for.  <a href="classpcl_1_1_keypoint.html#a9dbce2da2ea97dcb4f5635df33a129c9">更多...</a><br /></td></tr>
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<tr class="memitem:aa7b3aa0078d09857ed7afbee6b00e07b inherit pub_methods_classpcl_1_1_keypoint"><td class="memItemLeft" align="right" valign="top"><a id="aa7b3aa0078d09857ed7afbee6b00e07b"></a>
PointCloudInConstPtr&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_keypoint.html#aa7b3aa0078d09857ed7afbee6b00e07b">getSearchSurface</a> ()</td></tr>
<tr class="memdesc:aa7b3aa0078d09857ed7afbee6b00e07b inherit pub_methods_classpcl_1_1_keypoint"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get a pointer to the surface point cloud dataset. <br /></td></tr>
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<tr class="memitem:a153d731738fd5966e169e26b61013689 inherit pub_methods_classpcl_1_1_keypoint"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_keypoint.html#a153d731738fd5966e169e26b61013689">setSearchMethod</a> (const KdTreePtr &amp;tree)</td></tr>
<tr class="memdesc:a153d731738fd5966e169e26b61013689 inherit pub_methods_classpcl_1_1_keypoint"><td class="mdescLeft">&#160;</td><td class="mdescRight">Provide a pointer to the search object.  <a href="classpcl_1_1_keypoint.html#a153d731738fd5966e169e26b61013689">更多...</a><br /></td></tr>
<tr class="separator:a153d731738fd5966e169e26b61013689 inherit pub_methods_classpcl_1_1_keypoint"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a9c5fa8a8d95367a21111efe0b197a3c9 inherit pub_methods_classpcl_1_1_keypoint"><td class="memItemLeft" align="right" valign="top"><a id="a9c5fa8a8d95367a21111efe0b197a3c9"></a>
KdTreePtr&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_keypoint.html#a9c5fa8a8d95367a21111efe0b197a3c9">getSearchMethod</a> ()</td></tr>
<tr class="memdesc:a9c5fa8a8d95367a21111efe0b197a3c9 inherit pub_methods_classpcl_1_1_keypoint"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get a pointer to the search method used. <br /></td></tr>
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<tr class="memitem:addd331bafa986670ef416554728bd6a4 inherit pub_methods_classpcl_1_1_keypoint"><td class="memItemLeft" align="right" valign="top"><a id="addd331bafa986670ef416554728bd6a4"></a>
double&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_keypoint.html#addd331bafa986670ef416554728bd6a4">getSearchParameter</a> ()</td></tr>
<tr class="memdesc:addd331bafa986670ef416554728bd6a4 inherit pub_methods_classpcl_1_1_keypoint"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get the internal search parameter. <br /></td></tr>
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<tr class="memitem:a4de1c1a1545be786c12ec36fea4008df inherit pub_methods_classpcl_1_1_keypoint"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_keypoint.html#a4de1c1a1545be786c12ec36fea4008df">setKSearch</a> (int k)</td></tr>
<tr class="memdesc:a4de1c1a1545be786c12ec36fea4008df inherit pub_methods_classpcl_1_1_keypoint"><td class="mdescLeft">&#160;</td><td class="mdescRight">Set the number of k nearest neighbors to use for the feature estimation.  <a href="classpcl_1_1_keypoint.html#a4de1c1a1545be786c12ec36fea4008df">更多...</a><br /></td></tr>
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<tr class="memitem:a9a337c89ee7ce0816b8a677b5d538592 inherit pub_methods_classpcl_1_1_keypoint"><td class="memItemLeft" align="right" valign="top"><a id="a9a337c89ee7ce0816b8a677b5d538592"></a>
int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_keypoint.html#a9a337c89ee7ce0816b8a677b5d538592">getKSearch</a> ()</td></tr>
<tr class="memdesc:a9a337c89ee7ce0816b8a677b5d538592 inherit pub_methods_classpcl_1_1_keypoint"><td class="mdescLeft">&#160;</td><td class="mdescRight">get the number of k nearest neighbors used for the feature estimation. <br /></td></tr>
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<tr class="memitem:acb901dd8879ee9a34b3a87d1cb6d6a37 inherit pub_methods_classpcl_1_1_keypoint"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_keypoint.html#acb901dd8879ee9a34b3a87d1cb6d6a37">setRadiusSearch</a> (double radius)</td></tr>
<tr class="memdesc:acb901dd8879ee9a34b3a87d1cb6d6a37 inherit pub_methods_classpcl_1_1_keypoint"><td class="mdescLeft">&#160;</td><td class="mdescRight">Set the sphere radius that is to be used for determining the nearest neighbors used for the key point detection  <a href="classpcl_1_1_keypoint.html#acb901dd8879ee9a34b3a87d1cb6d6a37">更多...</a><br /></td></tr>
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<tr class="memitem:a9b6fb2953dc9be88c74d95c8446937bb inherit pub_methods_classpcl_1_1_keypoint"><td class="memItemLeft" align="right" valign="top"><a id="a9b6fb2953dc9be88c74d95c8446937bb"></a>
double&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_keypoint.html#a9b6fb2953dc9be88c74d95c8446937bb">getRadiusSearch</a> ()</td></tr>
<tr class="memdesc:a9b6fb2953dc9be88c74d95c8446937bb inherit pub_methods_classpcl_1_1_keypoint"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get the sphere radius used for determining the neighbors. <br /></td></tr>
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<tr class="memitem:a3ead3b82282115d4b7e400daee213c2d inherit pub_methods_classpcl_1_1_keypoint"><td class="memItemLeft" align="right" valign="top">pcl::PointIndicesConstPtr&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_keypoint.html#a3ead3b82282115d4b7e400daee213c2d">getKeypointsIndices</a> ()</td></tr>
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<tr class="memitem:a046a7cd11598b06d2076da69a3100e4b inherit pub_methods_classpcl_1_1_keypoint"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_keypoint.html#a046a7cd11598b06d2076da69a3100e4b">compute</a> (<a class="el" href="classpcl_1_1_point_cloud.html">PointCloudOut</a> &amp;output)</td></tr>
<tr class="memdesc:a046a7cd11598b06d2076da69a3100e4b inherit pub_methods_classpcl_1_1_keypoint"><td class="mdescLeft">&#160;</td><td class="mdescRight">Base method for key point detection for all points given in &lt;setInputCloud (), setIndices ()&gt; using the surface in setSearchSurface () and the spatial locator in setSearchMethod ()  <a href="classpcl_1_1_keypoint.html#a046a7cd11598b06d2076da69a3100e4b">更多...</a><br /></td></tr>
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<tr class="memitem:aa327f241d246cec23ca531261f60da3f inherit pub_methods_classpcl_1_1_keypoint"><td class="memItemLeft" align="right" valign="top">int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_keypoint.html#aa327f241d246cec23ca531261f60da3f">searchForNeighbors</a> (int index, double parameter, std::vector&lt; int &gt; &amp;indices, std::vector&lt; float &gt; &amp;distances) const</td></tr>
<tr class="memdesc:aa327f241d246cec23ca531261f60da3f inherit pub_methods_classpcl_1_1_keypoint"><td class="mdescLeft">&#160;</td><td class="mdescRight">Search for k-nearest neighbors using the spatial locator from <em>setSearchmethod</em>, and the given surface from <em>setSearchSurface</em>.  <a href="classpcl_1_1_keypoint.html#aa327f241d246cec23ca531261f60da3f">更多...</a><br /></td></tr>
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<tr class="inherit_header pub_methods_classpcl_1_1_p_c_l_base"><td colspan="2" onclick="javascript:toggleInherit('pub_methods_classpcl_1_1_p_c_l_base')"><img src="closed.png" alt="-"/>&#160;Public 成员函数 继承自 <a class="el" href="classpcl_1_1_p_c_l_base.html">pcl::PCLBase&lt; PointInT &gt;</a></td></tr>
<tr class="memitem:af4fbc5eb005057f8a0fc6d60bde595df inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="memItemLeft" align="right" valign="top"><a id="af4fbc5eb005057f8a0fc6d60bde595df"></a>
&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_p_c_l_base.html#af4fbc5eb005057f8a0fc6d60bde595df">PCLBase</a> ()</td></tr>
<tr class="memdesc:af4fbc5eb005057f8a0fc6d60bde595df inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="mdescLeft">&#160;</td><td class="mdescRight">Empty constructor. <br /></td></tr>
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<tr class="memitem:a7a6dd7a91275d7737cf1b18005b47244 inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="memItemLeft" align="right" valign="top"><a id="a7a6dd7a91275d7737cf1b18005b47244"></a>
&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_p_c_l_base.html#a7a6dd7a91275d7737cf1b18005b47244">PCLBase</a> (const <a class="el" href="classpcl_1_1_p_c_l_base.html">PCLBase</a> &amp;base)</td></tr>
<tr class="memdesc:a7a6dd7a91275d7737cf1b18005b47244 inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="mdescLeft">&#160;</td><td class="mdescRight">Copy constructor. <br /></td></tr>
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<tr class="memitem:ad5d6846e98e59c37dcc3dc9958d53966 inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="memItemLeft" align="right" valign="top"><a id="ad5d6846e98e59c37dcc3dc9958d53966"></a>
virtual&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_p_c_l_base.html#ad5d6846e98e59c37dcc3dc9958d53966">~PCLBase</a> ()</td></tr>
<tr class="memdesc:ad5d6846e98e59c37dcc3dc9958d53966 inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="mdescLeft">&#160;</td><td class="mdescRight">Destructor. <br /></td></tr>
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<tr class="memitem:a1952d7101f3942bac3b69ed55c1ca7ea inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="memItemLeft" align="right" valign="top">virtual void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_p_c_l_base.html#a1952d7101f3942bac3b69ed55c1ca7ea">setInputCloud</a> (const PointCloudConstPtr &amp;cloud)</td></tr>
<tr class="memdesc:a1952d7101f3942bac3b69ed55c1ca7ea inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="mdescLeft">&#160;</td><td class="mdescRight">Provide a pointer to the input dataset  <a href="classpcl_1_1_p_c_l_base.html#a1952d7101f3942bac3b69ed55c1ca7ea">更多...</a><br /></td></tr>
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<tr class="memitem:a8cd745c4f7a792212f4fc3720b9d46ea inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="memItemLeft" align="right" valign="top"><a id="a8cd745c4f7a792212f4fc3720b9d46ea"></a>
PointCloudConstPtr const&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_p_c_l_base.html#a8cd745c4f7a792212f4fc3720b9d46ea">getInputCloud</a> () const</td></tr>
<tr class="memdesc:a8cd745c4f7a792212f4fc3720b9d46ea inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get a pointer to the input point cloud dataset. <br /></td></tr>
<tr class="separator:a8cd745c4f7a792212f4fc3720b9d46ea inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ab219359de6eb34c9d51e2e976dd1a0d1 inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="memItemLeft" align="right" valign="top">virtual void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_p_c_l_base.html#ab219359de6eb34c9d51e2e976dd1a0d1">setIndices</a> (const IndicesPtr &amp;indices)</td></tr>
<tr class="memdesc:ab219359de6eb34c9d51e2e976dd1a0d1 inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="mdescLeft">&#160;</td><td class="mdescRight">Provide a pointer to the vector of indices that represents the input data.  <a href="classpcl_1_1_p_c_l_base.html#ab219359de6eb34c9d51e2e976dd1a0d1">更多...</a><br /></td></tr>
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<tr class="memitem:a436c68c74b31e4dd00000adfbb11ca7c inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="memItemLeft" align="right" valign="top">virtual void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_p_c_l_base.html#a436c68c74b31e4dd00000adfbb11ca7c">setIndices</a> (const IndicesConstPtr &amp;indices)</td></tr>
<tr class="memdesc:a436c68c74b31e4dd00000adfbb11ca7c inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="mdescLeft">&#160;</td><td class="mdescRight">Provide a pointer to the vector of indices that represents the input data.  <a href="classpcl_1_1_p_c_l_base.html#a436c68c74b31e4dd00000adfbb11ca7c">更多...</a><br /></td></tr>
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<tr class="memitem:af9cc90d8364ce968566f75800d3773ca inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="memItemLeft" align="right" valign="top">virtual void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_p_c_l_base.html#af9cc90d8364ce968566f75800d3773ca">setIndices</a> (const PointIndicesConstPtr &amp;indices)</td></tr>
<tr class="memdesc:af9cc90d8364ce968566f75800d3773ca inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="mdescLeft">&#160;</td><td class="mdescRight">Provide a pointer to the vector of indices that represents the input data.  <a href="classpcl_1_1_p_c_l_base.html#af9cc90d8364ce968566f75800d3773ca">更多...</a><br /></td></tr>
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<tr class="memitem:a930c7a6375fdf65ff8cfdb4eb4a6d996 inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="memItemLeft" align="right" valign="top">virtual void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_p_c_l_base.html#a930c7a6375fdf65ff8cfdb4eb4a6d996">setIndices</a> (size_t row_start, size_t col_start, size_t nb_rows, size_t nb_cols)</td></tr>
<tr class="memdesc:a930c7a6375fdf65ff8cfdb4eb4a6d996 inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="mdescLeft">&#160;</td><td class="mdescRight">Set the indices for the points laying within an interest region of the point cloud.  <a href="classpcl_1_1_p_c_l_base.html#a930c7a6375fdf65ff8cfdb4eb4a6d996">更多...</a><br /></td></tr>
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<tr class="memitem:a058753dd4de73d3d0062fe2e452fba3c inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="memItemLeft" align="right" valign="top"><a id="a058753dd4de73d3d0062fe2e452fba3c"></a>
IndicesPtr const&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_p_c_l_base.html#a058753dd4de73d3d0062fe2e452fba3c">getIndices</a> ()</td></tr>
<tr class="memdesc:a058753dd4de73d3d0062fe2e452fba3c inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get a pointer to the vector of indices used. <br /></td></tr>
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<tr class="memitem:acae187b37230758959572ceb1e6e2045 inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="memItemLeft" align="right" valign="top"><a id="acae187b37230758959572ceb1e6e2045"></a>
IndicesConstPtr const&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_p_c_l_base.html#acae187b37230758959572ceb1e6e2045">getIndices</a> () const</td></tr>
<tr class="memdesc:acae187b37230758959572ceb1e6e2045 inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get a pointer to the vector of indices used. <br /></td></tr>
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<tr class="memitem:af7335fedb0af0930b9d1dedcb54ba201 inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="memItemLeft" align="right" valign="top">const PointInT &amp;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_p_c_l_base.html#af7335fedb0af0930b9d1dedcb54ba201">operator[]</a> (size_t pos) const</td></tr>
<tr class="memdesc:af7335fedb0af0930b9d1dedcb54ba201 inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="mdescLeft">&#160;</td><td class="mdescRight">Override PointCloud operator[] to shorten code  <a href="classpcl_1_1_p_c_l_base.html#af7335fedb0af0930b9d1dedcb54ba201">更多...</a><br /></td></tr>
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</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pro-methods"></a>
Protected 成员函数</h2></td></tr>
<tr class="memitem:af42151005f29dff409321da2c1dc228f"><td class="memItemLeft" align="right" valign="top"><a id="af42151005f29dff409321da2c1dc228f"></a>
bool&#160;</td><td class="memItemRight" valign="bottom"><b>initCompute</b> ()</td></tr>
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<tr class="memitem:a9ce260543a3fe87f0e70c1b6f8fd9b28"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_s_i_f_t_keypoint.html#a9ce260543a3fe87f0e70c1b6f8fd9b28">detectKeypoints</a> (<a class="el" href="classpcl_1_1_point_cloud.html">PointCloudOut</a> &amp;output)</td></tr>
<tr class="memdesc:a9ce260543a3fe87f0e70c1b6f8fd9b28"><td class="mdescLeft">&#160;</td><td class="mdescRight">Detect the SIFT keypoints for a set of points given in setInputCloud () using the spatial locator in setSearchMethod ().  <a href="classpcl_1_1_s_i_f_t_keypoint.html#a9ce260543a3fe87f0e70c1b6f8fd9b28">更多...</a><br /></td></tr>
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<tr class="inherit_header pro_methods_classpcl_1_1_keypoint"><td colspan="2" onclick="javascript:toggleInherit('pro_methods_classpcl_1_1_keypoint')"><img src="closed.png" alt="-"/>&#160;Protected 成员函数 继承自 <a class="el" href="classpcl_1_1_keypoint.html">pcl::Keypoint&lt; PointInT, PointOutT &gt;</a></td></tr>
<tr class="memitem:af2dd9244731bfd903ce4aa73efb996eb inherit pro_methods_classpcl_1_1_keypoint"><td class="memItemLeft" align="right" valign="top"><a id="af2dd9244731bfd903ce4aa73efb996eb"></a>
const std::string &amp;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_keypoint.html#af2dd9244731bfd903ce4aa73efb996eb">getClassName</a> () const</td></tr>
<tr class="memdesc:af2dd9244731bfd903ce4aa73efb996eb inherit pro_methods_classpcl_1_1_keypoint"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get a string representation of the name of this class. <br /></td></tr>
<tr class="separator:af2dd9244731bfd903ce4aa73efb996eb inherit pro_methods_classpcl_1_1_keypoint"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="inherit_header pro_methods_classpcl_1_1_p_c_l_base"><td colspan="2" onclick="javascript:toggleInherit('pro_methods_classpcl_1_1_p_c_l_base')"><img src="closed.png" alt="-"/>&#160;Protected 成员函数 继承自 <a class="el" href="classpcl_1_1_p_c_l_base.html">pcl::PCLBase&lt; PointInT &gt;</a></td></tr>
<tr class="memitem:acceb20854934f4cf77e266eb5a44d4f0 inherit pro_methods_classpcl_1_1_p_c_l_base"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_p_c_l_base.html#acceb20854934f4cf77e266eb5a44d4f0">initCompute</a> ()</td></tr>
<tr class="memdesc:acceb20854934f4cf77e266eb5a44d4f0 inherit pro_methods_classpcl_1_1_p_c_l_base"><td class="mdescLeft">&#160;</td><td class="mdescRight">This method should get called before starting the actual computation.  <a href="classpcl_1_1_p_c_l_base.html#acceb20854934f4cf77e266eb5a44d4f0">更多...</a><br /></td></tr>
<tr class="separator:acceb20854934f4cf77e266eb5a44d4f0 inherit pro_methods_classpcl_1_1_p_c_l_base"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:afc426c4eebb94b7734d4fa556bff1420 inherit pro_methods_classpcl_1_1_p_c_l_base"><td class="memItemLeft" align="right" valign="top"><a id="afc426c4eebb94b7734d4fa556bff1420"></a>
bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_p_c_l_base.html#afc426c4eebb94b7734d4fa556bff1420">deinitCompute</a> ()</td></tr>
<tr class="memdesc:afc426c4eebb94b7734d4fa556bff1420 inherit pro_methods_classpcl_1_1_p_c_l_base"><td class="mdescLeft">&#160;</td><td class="mdescRight">This method should get called after finishing the actual computation. <br /></td></tr>
<tr class="separator:afc426c4eebb94b7734d4fa556bff1420 inherit pro_methods_classpcl_1_1_p_c_l_base"><td class="memSeparator" colspan="2">&#160;</td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pri-methods"></a>
Private 成员函数</h2></td></tr>
<tr class="memitem:a2b424cc026e8d0a693697a22eb360cfc"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_s_i_f_t_keypoint.html#a2b424cc026e8d0a693697a22eb360cfc">detectKeypointsForOctave</a> (const <a class="el" href="classpcl_1_1_point_cloud.html">PointCloudIn</a> &amp;input, <a class="el" href="classpcl_1_1search_1_1_search.html">KdTree</a> &amp;tree, float base_scale, int nr_scales_per_octave, <a class="el" href="classpcl_1_1_point_cloud.html">PointCloudOut</a> &amp;output)</td></tr>
<tr class="memdesc:a2b424cc026e8d0a693697a22eb360cfc"><td class="mdescLeft">&#160;</td><td class="mdescRight">Detect the SIFT keypoints for a given point cloud for a single octave.  <a href="classpcl_1_1_s_i_f_t_keypoint.html#a2b424cc026e8d0a693697a22eb360cfc">更多...</a><br /></td></tr>
<tr class="separator:a2b424cc026e8d0a693697a22eb360cfc"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a7b381aa10dc8c1701e7f1764a620bb8a"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_s_i_f_t_keypoint.html#a7b381aa10dc8c1701e7f1764a620bb8a">computeScaleSpace</a> (const <a class="el" href="classpcl_1_1_point_cloud.html">PointCloudIn</a> &amp;input, <a class="el" href="classpcl_1_1search_1_1_search.html">KdTree</a> &amp;tree, const std::vector&lt; float &gt; &amp;scales, Eigen::MatrixXf &amp;diff_of_gauss)</td></tr>
<tr class="memdesc:a7b381aa10dc8c1701e7f1764a620bb8a"><td class="mdescLeft">&#160;</td><td class="mdescRight">Compute the difference-of-Gaussian (DoG) scale space for the given input and scales  <a href="classpcl_1_1_s_i_f_t_keypoint.html#a7b381aa10dc8c1701e7f1764a620bb8a">更多...</a><br /></td></tr>
<tr class="separator:a7b381aa10dc8c1701e7f1764a620bb8a"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a2523f0256ba669b659a039da1b18a95c"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_s_i_f_t_keypoint.html#a2523f0256ba669b659a039da1b18a95c">findScaleSpaceExtrema</a> (const <a class="el" href="classpcl_1_1_point_cloud.html">PointCloudIn</a> &amp;input, <a class="el" href="classpcl_1_1search_1_1_search.html">KdTree</a> &amp;tree, const Eigen::MatrixXf &amp;diff_of_gauss, std::vector&lt; int &gt; &amp;extrema_indices, std::vector&lt; int &gt; &amp;extrema_scales)</td></tr>
<tr class="memdesc:a2523f0256ba669b659a039da1b18a95c"><td class="mdescLeft">&#160;</td><td class="mdescRight">Find the local minima and maxima in the provided difference-of-Gaussian (DoG) scale space  <a href="classpcl_1_1_s_i_f_t_keypoint.html#a2523f0256ba669b659a039da1b18a95c">更多...</a><br /></td></tr>
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</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pri-attribs"></a>
Private 属性</h2></td></tr>
<tr class="memitem:a79b4f1c9356c27051d7ec7a2c471b991"><td class="memItemLeft" align="right" valign="top"><a id="a79b4f1c9356c27051d7ec7a2c471b991"></a>
float&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_s_i_f_t_keypoint.html#a79b4f1c9356c27051d7ec7a2c471b991">min_scale_</a></td></tr>
<tr class="memdesc:a79b4f1c9356c27051d7ec7a2c471b991"><td class="mdescLeft">&#160;</td><td class="mdescRight">The standard deviation of the smallest scale in the scale space. <br /></td></tr>
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<tr class="memitem:ab907e9fe76da88d568ab5d1c8151d6d9"><td class="memItemLeft" align="right" valign="top"><a id="ab907e9fe76da88d568ab5d1c8151d6d9"></a>
int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_s_i_f_t_keypoint.html#ab907e9fe76da88d568ab5d1c8151d6d9">nr_octaves_</a></td></tr>
<tr class="memdesc:ab907e9fe76da88d568ab5d1c8151d6d9"><td class="mdescLeft">&#160;</td><td class="mdescRight">The number of octaves (i.e. doublings of scale) over which to search for keypoints. <br /></td></tr>
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<tr class="memitem:a259a9b80428da022653896a1c16372cc"><td class="memItemLeft" align="right" valign="top"><a id="a259a9b80428da022653896a1c16372cc"></a>
int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_s_i_f_t_keypoint.html#a259a9b80428da022653896a1c16372cc">nr_scales_per_octave_</a></td></tr>
<tr class="memdesc:a259a9b80428da022653896a1c16372cc"><td class="mdescLeft">&#160;</td><td class="mdescRight">The number of scales to be computed for each octave. <br /></td></tr>
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<tr class="memitem:ac982d9e5cb07edb578b262e588c08841"><td class="memItemLeft" align="right" valign="top"><a id="ac982d9e5cb07edb578b262e588c08841"></a>
float&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_s_i_f_t_keypoint.html#ac982d9e5cb07edb578b262e588c08841">min_contrast_</a></td></tr>
<tr class="memdesc:ac982d9e5cb07edb578b262e588c08841"><td class="mdescLeft">&#160;</td><td class="mdescRight">The minimum contrast required for detection. <br /></td></tr>
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<tr class="memitem:a49af88108a4e7a9e769185be0c5dc84a"><td class="memItemLeft" align="right" valign="top"><a id="a49af88108a4e7a9e769185be0c5dc84a"></a>
int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_s_i_f_t_keypoint.html#a49af88108a4e7a9e769185be0c5dc84a">scale_idx_</a></td></tr>
<tr class="memdesc:a49af88108a4e7a9e769185be0c5dc84a"><td class="mdescLeft">&#160;</td><td class="mdescRight">Set to a value different than -1 if the output cloud has a "scale" field and we have to save the keypoints scales. <br /></td></tr>
<tr class="separator:a49af88108a4e7a9e769185be0c5dc84a"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:abe6d16ca2a452c065d83f32f7a9416d6"><td class="memItemLeft" align="right" valign="top"><a id="abe6d16ca2a452c065d83f32f7a9416d6"></a>
std::vector&lt; <a class="el" href="structpcl_1_1_p_c_l_point_field.html">pcl::PCLPointField</a> &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_s_i_f_t_keypoint.html#abe6d16ca2a452c065d83f32f7a9416d6">out_fields_</a></td></tr>
<tr class="memdesc:abe6d16ca2a452c065d83f32f7a9416d6"><td class="mdescLeft">&#160;</td><td class="mdescRight">The list of fields present in the output point cloud data. <br /></td></tr>
<tr class="separator:abe6d16ca2a452c065d83f32f7a9416d6"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a8ae33bd5f369d1891ccc678d8fc8092d"><td class="memItemLeft" align="right" valign="top"><a id="a8ae33bd5f369d1891ccc678d8fc8092d"></a>
<a class="el" href="structpcl_1_1_s_i_f_t_keypoint_field_selector.html">SIFTKeypointFieldSelector</a>&lt; PointInT &gt;&#160;</td><td class="memItemRight" valign="bottom"><b>getFieldValue_</b></td></tr>
<tr class="separator:a8ae33bd5f369d1891ccc678d8fc8092d"><td class="memSeparator" colspan="2">&#160;</td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="inherited"></a>
额外继承的成员函数</h2></td></tr>
<tr class="inherit_header pro_attribs_classpcl_1_1_keypoint"><td colspan="2" onclick="javascript:toggleInherit('pro_attribs_classpcl_1_1_keypoint')"><img src="closed.png" alt="-"/>&#160;Protected 属性 继承自 <a class="el" href="classpcl_1_1_keypoint.html">pcl::Keypoint&lt; PointInT, PointOutT &gt;</a></td></tr>
<tr class="memitem:aef5710b81d66e6d429a9dfeb41d606ff inherit pro_attribs_classpcl_1_1_keypoint"><td class="memItemLeft" align="right" valign="top"><a id="aef5710b81d66e6d429a9dfeb41d606ff"></a>
std::string&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_keypoint.html#aef5710b81d66e6d429a9dfeb41d606ff">name_</a></td></tr>
<tr class="memdesc:aef5710b81d66e6d429a9dfeb41d606ff inherit pro_attribs_classpcl_1_1_keypoint"><td class="mdescLeft">&#160;</td><td class="mdescRight">The key point detection method's name. <br /></td></tr>
<tr class="separator:aef5710b81d66e6d429a9dfeb41d606ff inherit pro_attribs_classpcl_1_1_keypoint"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:aa2ee2830db93620925b82bf6b8ffea63 inherit pro_attribs_classpcl_1_1_keypoint"><td class="memItemLeft" align="right" valign="top"><a id="aa2ee2830db93620925b82bf6b8ffea63"></a>
SearchMethod&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_keypoint.html#aa2ee2830db93620925b82bf6b8ffea63">search_method_</a></td></tr>
<tr class="memdesc:aa2ee2830db93620925b82bf6b8ffea63 inherit pro_attribs_classpcl_1_1_keypoint"><td class="mdescLeft">&#160;</td><td class="mdescRight">The search method template for indices. <br /></td></tr>
<tr class="separator:aa2ee2830db93620925b82bf6b8ffea63 inherit pro_attribs_classpcl_1_1_keypoint"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a642e18edc66cffd15266bf9e7bf20bec inherit pro_attribs_classpcl_1_1_keypoint"><td class="memItemLeft" align="right" valign="top"><a id="a642e18edc66cffd15266bf9e7bf20bec"></a>
SearchMethodSurface&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_keypoint.html#a642e18edc66cffd15266bf9e7bf20bec">search_method_surface_</a></td></tr>
<tr class="memdesc:a642e18edc66cffd15266bf9e7bf20bec inherit pro_attribs_classpcl_1_1_keypoint"><td class="mdescLeft">&#160;</td><td class="mdescRight">The search method template for points. <br /></td></tr>
<tr class="separator:a642e18edc66cffd15266bf9e7bf20bec inherit pro_attribs_classpcl_1_1_keypoint"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a1998c46514f3f8d8d7665f6b505ca389 inherit pro_attribs_classpcl_1_1_keypoint"><td class="memItemLeft" align="right" valign="top"><a id="a1998c46514f3f8d8d7665f6b505ca389"></a>
PointCloudInConstPtr&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_keypoint.html#a1998c46514f3f8d8d7665f6b505ca389">surface_</a></td></tr>
<tr class="memdesc:a1998c46514f3f8d8d7665f6b505ca389 inherit pro_attribs_classpcl_1_1_keypoint"><td class="mdescLeft">&#160;</td><td class="mdescRight">An input point cloud describing the surface that is to be used for nearest neighbors estimation. <br /></td></tr>
<tr class="separator:a1998c46514f3f8d8d7665f6b505ca389 inherit pro_attribs_classpcl_1_1_keypoint"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a9bca452f3813c88d994dba18d4a7a1c8 inherit pro_attribs_classpcl_1_1_keypoint"><td class="memItemLeft" align="right" valign="top"><a id="a9bca452f3813c88d994dba18d4a7a1c8"></a>
KdTreePtr&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_keypoint.html#a9bca452f3813c88d994dba18d4a7a1c8">tree_</a></td></tr>
<tr class="memdesc:a9bca452f3813c88d994dba18d4a7a1c8 inherit pro_attribs_classpcl_1_1_keypoint"><td class="mdescLeft">&#160;</td><td class="mdescRight">A pointer to the spatial search object. <br /></td></tr>
<tr class="separator:a9bca452f3813c88d994dba18d4a7a1c8 inherit pro_attribs_classpcl_1_1_keypoint"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a578368dcf8cacc6770aa5f7e7a306def inherit pro_attribs_classpcl_1_1_keypoint"><td class="memItemLeft" align="right" valign="top"><a id="a578368dcf8cacc6770aa5f7e7a306def"></a>
double&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_keypoint.html#a578368dcf8cacc6770aa5f7e7a306def">search_parameter_</a></td></tr>
<tr class="memdesc:a578368dcf8cacc6770aa5f7e7a306def inherit pro_attribs_classpcl_1_1_keypoint"><td class="mdescLeft">&#160;</td><td class="mdescRight">The actual search parameter (casted from either <em>search_radius_</em> or <em>k_</em>). <br /></td></tr>
<tr class="separator:a578368dcf8cacc6770aa5f7e7a306def inherit pro_attribs_classpcl_1_1_keypoint"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a3bf7980bf7404eb9b5571afbbc2001f5 inherit pro_attribs_classpcl_1_1_keypoint"><td class="memItemLeft" align="right" valign="top"><a id="a3bf7980bf7404eb9b5571afbbc2001f5"></a>
double&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_keypoint.html#a3bf7980bf7404eb9b5571afbbc2001f5">search_radius_</a></td></tr>
<tr class="memdesc:a3bf7980bf7404eb9b5571afbbc2001f5 inherit pro_attribs_classpcl_1_1_keypoint"><td class="mdescLeft">&#160;</td><td class="mdescRight">The nearest neighbors search radius for each point. <br /></td></tr>
<tr class="separator:a3bf7980bf7404eb9b5571afbbc2001f5 inherit pro_attribs_classpcl_1_1_keypoint"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a93359dca5b099e843c3cf0d0db1c59e1 inherit pro_attribs_classpcl_1_1_keypoint"><td class="memItemLeft" align="right" valign="top"><a id="a93359dca5b099e843c3cf0d0db1c59e1"></a>
int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_keypoint.html#a93359dca5b099e843c3cf0d0db1c59e1">k_</a></td></tr>
<tr class="memdesc:a93359dca5b099e843c3cf0d0db1c59e1 inherit pro_attribs_classpcl_1_1_keypoint"><td class="mdescLeft">&#160;</td><td class="mdescRight">The number of K nearest neighbors to use for each point. <br /></td></tr>
<tr class="separator:a93359dca5b099e843c3cf0d0db1c59e1 inherit pro_attribs_classpcl_1_1_keypoint"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:abe8df9e1d66f449bc478cc06f062f209 inherit pro_attribs_classpcl_1_1_keypoint"><td class="memItemLeft" align="right" valign="top"><a id="abe8df9e1d66f449bc478cc06f062f209"></a>
pcl::PointIndicesPtr&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_keypoint.html#abe8df9e1d66f449bc478cc06f062f209">keypoints_indices_</a></td></tr>
<tr class="memdesc:abe8df9e1d66f449bc478cc06f062f209 inherit pro_attribs_classpcl_1_1_keypoint"><td class="mdescLeft">&#160;</td><td class="mdescRight">Indices of the keypoints in the input cloud. <br /></td></tr>
<tr class="separator:abe8df9e1d66f449bc478cc06f062f209 inherit pro_attribs_classpcl_1_1_keypoint"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="inherit_header pro_attribs_classpcl_1_1_p_c_l_base"><td colspan="2" onclick="javascript:toggleInherit('pro_attribs_classpcl_1_1_p_c_l_base')"><img src="closed.png" alt="-"/>&#160;Protected 属性 继承自 <a class="el" href="classpcl_1_1_p_c_l_base.html">pcl::PCLBase&lt; PointInT &gt;</a></td></tr>
<tr class="memitem:a09c70d8e06e3fb4f07903fe6f8d67869 inherit pro_attribs_classpcl_1_1_p_c_l_base"><td class="memItemLeft" align="right" valign="top"><a id="a09c70d8e06e3fb4f07903fe6f8d67869"></a>
PointCloudConstPtr&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_p_c_l_base.html#a09c70d8e06e3fb4f07903fe6f8d67869">input_</a></td></tr>
<tr class="memdesc:a09c70d8e06e3fb4f07903fe6f8d67869 inherit pro_attribs_classpcl_1_1_p_c_l_base"><td class="mdescLeft">&#160;</td><td class="mdescRight">The input point cloud dataset. <br /></td></tr>
<tr class="separator:a09c70d8e06e3fb4f07903fe6f8d67869 inherit pro_attribs_classpcl_1_1_p_c_l_base"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:aaee847c8a517ebf365bad2cb182a6626 inherit pro_attribs_classpcl_1_1_p_c_l_base"><td class="memItemLeft" align="right" valign="top"><a id="aaee847c8a517ebf365bad2cb182a6626"></a>
IndicesPtr&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_p_c_l_base.html#aaee847c8a517ebf365bad2cb182a6626">indices_</a></td></tr>
<tr class="memdesc:aaee847c8a517ebf365bad2cb182a6626 inherit pro_attribs_classpcl_1_1_p_c_l_base"><td class="mdescLeft">&#160;</td><td class="mdescRight">A pointer to the vector of point indices to use. <br /></td></tr>
<tr class="separator:aaee847c8a517ebf365bad2cb182a6626 inherit pro_attribs_classpcl_1_1_p_c_l_base"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ada1eadb824d34ca9206a86343d9760bb inherit pro_attribs_classpcl_1_1_p_c_l_base"><td class="memItemLeft" align="right" valign="top"><a id="ada1eadb824d34ca9206a86343d9760bb"></a>
bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_p_c_l_base.html#ada1eadb824d34ca9206a86343d9760bb">use_indices_</a></td></tr>
<tr class="memdesc:ada1eadb824d34ca9206a86343d9760bb inherit pro_attribs_classpcl_1_1_p_c_l_base"><td class="mdescLeft">&#160;</td><td class="mdescRight">Set to true if point indices are used. <br /></td></tr>
<tr class="separator:ada1eadb824d34ca9206a86343d9760bb inherit pro_attribs_classpcl_1_1_p_c_l_base"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:adadb0299f144528020ed558af6879662 inherit pro_attribs_classpcl_1_1_p_c_l_base"><td class="memItemLeft" align="right" valign="top"><a id="adadb0299f144528020ed558af6879662"></a>
bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_p_c_l_base.html#adadb0299f144528020ed558af6879662">fake_indices_</a></td></tr>
<tr class="memdesc:adadb0299f144528020ed558af6879662 inherit pro_attribs_classpcl_1_1_p_c_l_base"><td class="mdescLeft">&#160;</td><td class="mdescRight">If no set of indices are given, we construct a set of fake indices that mimic the input PointCloud. <br /></td></tr>
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</table>
<a name="details" id="details"></a><h2 class="groupheader">详细描述</h2>
<div class="textblock"><h3>template&lt;typename PointInT, typename PointOutT&gt;<br />
class pcl::SIFTKeypoint&lt; PointInT, PointOutT &gt;</h3>

<p><b><a class="el" href="classpcl_1_1_s_i_f_t_keypoint.html" title="SIFTKeypoint detects the Scale Invariant Feature Transform keypoints for a given point cloud dataset ...">SIFTKeypoint</a></b> detects the Scale Invariant <a class="el" href="classpcl_1_1_feature.html" title="Feature represents the base feature class. Some generic 3D operations that are applicable to all feat...">Feature</a> Transform keypoints for a given point cloud dataset containing points and intensity. This implementation adapts the original algorithm from images to point clouds. </p>
<p>For more information about the image-based SIFT interest operator, see:</p>
<p>David G. Lowe, "Distinctive image features from scale-invariant keypoints," International Journal of Computer Vision, 60, 2 (2004), pp. 91-110.</p>
<dl class="section author"><dt>作者</dt><dd>Michael Dixon </dd></dl>
</div><h2 class="groupheader">成员函数说明</h2>
<a id="a7b381aa10dc8c1701e7f1764a620bb8a"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a7b381aa10dc8c1701e7f1764a620bb8a">&#9670;&nbsp;</a></span>computeScaleSpace()</h2>

<div class="memitem">
<div class="memproto">
<div class="memtemplate">
template&lt;typename PointInT , typename PointOutT &gt; </div>
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  <tr>
  <td class="mlabels-left">
      <table class="memname">
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          <td class="memname">void <a class="el" href="classpcl_1_1_s_i_f_t_keypoint.html">pcl::SIFTKeypoint</a>&lt; PointInT, PointOutT &gt;::computeScaleSpace </td>
          <td>(</td>
          <td class="paramtype">const <a class="el" href="classpcl_1_1_point_cloud.html">PointCloudIn</a> &amp;&#160;</td>
          <td class="paramname"><em>input</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype"><a class="el" href="classpcl_1_1search_1_1_search.html">KdTree</a> &amp;&#160;</td>
          <td class="paramname"><em>tree</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const std::vector&lt; float &gt; &amp;&#160;</td>
          <td class="paramname"><em>scales</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">Eigen::MatrixXf &amp;&#160;</td>
          <td class="paramname"><em>diff_of_gauss</em>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">private</span></span>  </td>
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<p>Compute the difference-of-Gaussian (DoG) scale space for the given input and scales </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramname">input</td><td>the point cloud for which the DoG scale space will be computed </td></tr>
    <tr><td class="paramname">tree</td><td>a k-D tree of the points in <em>input</em> </td></tr>
    <tr><td class="paramname">scales</td><td>a vector containing the scales over which to compute the DoG scale space </td></tr>
    <tr><td class="paramname">diff_of_gauss</td><td>the resultant DoG scale space (in a number-of-points by number-of-scales matrix) </td></tr>
  </table>
  </dd>
</dl>
<div class="fragment"><div class="line"><a name="l00214"></a><span class="lineno">  214</span>&#160;{</div>
<div class="line"><a name="l00215"></a><span class="lineno">  215</span>&#160;  diff_of_gauss.resize (input.size (), scales.size () - 1);</div>
<div class="line"><a name="l00216"></a><span class="lineno">  216</span>&#160; </div>
<div class="line"><a name="l00217"></a><span class="lineno">  217</span>&#160;  <span class="comment">// For efficiency, we will only filter over points within 3 standard deviations </span></div>
<div class="line"><a name="l00218"></a><span class="lineno">  218</span>&#160;  <span class="keyword">const</span> <span class="keywordtype">float</span> max_radius = 3.0f * scales.back ();</div>
<div class="line"><a name="l00219"></a><span class="lineno">  219</span>&#160; </div>
<div class="line"><a name="l00220"></a><span class="lineno">  220</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i_point = 0; i_point &lt; static_cast&lt;int&gt; (input.size ()); ++i_point)</div>
<div class="line"><a name="l00221"></a><span class="lineno">  221</span>&#160;  {</div>
<div class="line"><a name="l00222"></a><span class="lineno">  222</span>&#160;    std::vector&lt;int&gt; nn_indices;</div>
<div class="line"><a name="l00223"></a><span class="lineno">  223</span>&#160;    std::vector&lt;float&gt; nn_dist;</div>
<div class="line"><a name="l00224"></a><span class="lineno">  224</span>&#160;    tree.radiusSearch (i_point, max_radius, nn_indices, nn_dist); <span class="comment">// *</span></div>
<div class="line"><a name="l00225"></a><span class="lineno">  225</span>&#160;    <span class="comment">// * note: at this stage of the algorithm, we must find all points within a radius defined by the maximum scale, </span></div>
<div class="line"><a name="l00226"></a><span class="lineno">  226</span>&#160;    <span class="comment">//   regardless of the configurable search method specified by the user, so we directly employ tree.radiusSearch </span></div>
<div class="line"><a name="l00227"></a><span class="lineno">  227</span>&#160;    <span class="comment">//   here instead of using searchForNeighbors.</span></div>
<div class="line"><a name="l00228"></a><span class="lineno">  228</span>&#160; </div>
<div class="line"><a name="l00229"></a><span class="lineno">  229</span>&#160;    <span class="comment">// For each scale, compute the Gaussian &quot;filter response&quot; at the current point</span></div>
<div class="line"><a name="l00230"></a><span class="lineno">  230</span>&#160;    <span class="keywordtype">float</span> filter_response = 0.0f;</div>
<div class="line"><a name="l00231"></a><span class="lineno">  231</span>&#160;    <span class="keywordtype">float</span> previous_filter_response;</div>
<div class="line"><a name="l00232"></a><span class="lineno">  232</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i_scale = 0; i_scale &lt; scales.size (); ++i_scale)</div>
<div class="line"><a name="l00233"></a><span class="lineno">  233</span>&#160;    {</div>
<div class="line"><a name="l00234"></a><span class="lineno">  234</span>&#160;      <span class="keywordtype">float</span> sigma_sqr = powf (scales[i_scale], 2.0f);</div>
<div class="line"><a name="l00235"></a><span class="lineno">  235</span>&#160; </div>
<div class="line"><a name="l00236"></a><span class="lineno">  236</span>&#160;      <span class="keywordtype">float</span> numerator = 0.0f;</div>
<div class="line"><a name="l00237"></a><span class="lineno">  237</span>&#160;      <span class="keywordtype">float</span> denominator = 0.0f;</div>
<div class="line"><a name="l00238"></a><span class="lineno">  238</span>&#160;      <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i_neighbor = 0; i_neighbor &lt; nn_indices.size (); ++i_neighbor)</div>
<div class="line"><a name="l00239"></a><span class="lineno">  239</span>&#160;      {</div>
<div class="line"><a name="l00240"></a><span class="lineno">  240</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">float</span> &amp;value = getFieldValue_ (input.points[nn_indices[i_neighbor]]);</div>
<div class="line"><a name="l00241"></a><span class="lineno">  241</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">float</span> &amp;dist_sqr = nn_dist[i_neighbor];</div>
<div class="line"><a name="l00242"></a><span class="lineno">  242</span>&#160;        <span class="keywordflow">if</span> (dist_sqr &lt;= 9*sigma_sqr)</div>
<div class="line"><a name="l00243"></a><span class="lineno">  243</span>&#160;        {</div>
<div class="line"><a name="l00244"></a><span class="lineno">  244</span>&#160;          <span class="keywordtype">float</span> w = expf (-0.5f * dist_sqr / sigma_sqr);</div>
<div class="line"><a name="l00245"></a><span class="lineno">  245</span>&#160;          numerator += value * w;</div>
<div class="line"><a name="l00246"></a><span class="lineno">  246</span>&#160;          denominator += w;</div>
<div class="line"><a name="l00247"></a><span class="lineno">  247</span>&#160;        }</div>
<div class="line"><a name="l00248"></a><span class="lineno">  248</span>&#160;        <span class="keywordflow">else</span> <span class="keywordflow">break</span>; <span class="comment">// i.e. if dist &gt; 3 standard deviations, then terminate early</span></div>
<div class="line"><a name="l00249"></a><span class="lineno">  249</span>&#160;      }</div>
<div class="line"><a name="l00250"></a><span class="lineno">  250</span>&#160;      previous_filter_response = filter_response;</div>
<div class="line"><a name="l00251"></a><span class="lineno">  251</span>&#160;      filter_response = numerator / denominator;</div>
<div class="line"><a name="l00252"></a><span class="lineno">  252</span>&#160; </div>
<div class="line"><a name="l00253"></a><span class="lineno">  253</span>&#160;      <span class="comment">// Compute the difference between adjacent scales</span></div>
<div class="line"><a name="l00254"></a><span class="lineno">  254</span>&#160;      <span class="keywordflow">if</span> (i_scale &gt; 0)</div>
<div class="line"><a name="l00255"></a><span class="lineno">  255</span>&#160;        diff_of_gauss (i_point, i_scale - 1) = filter_response - previous_filter_response;</div>
<div class="line"><a name="l00256"></a><span class="lineno">  256</span>&#160;    }</div>
<div class="line"><a name="l00257"></a><span class="lineno">  257</span>&#160;  }</div>
<div class="line"><a name="l00258"></a><span class="lineno">  258</span>&#160;}</div>
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<a id="a9ce260543a3fe87f0e70c1b6f8fd9b28"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a9ce260543a3fe87f0e70c1b6f8fd9b28">&#9670;&nbsp;</a></span>detectKeypoints()</h2>

<div class="memitem">
<div class="memproto">
<div class="memtemplate">
template&lt;typename PointInT , typename PointOutT &gt; </div>
<table class="mlabels">
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      <table class="memname">
        <tr>
          <td class="memname">void <a class="el" href="classpcl_1_1_s_i_f_t_keypoint.html">pcl::SIFTKeypoint</a>&lt; PointInT, PointOutT &gt;::detectKeypoints </td>
          <td>(</td>
          <td class="paramtype"><a class="el" href="classpcl_1_1_point_cloud.html">PointCloudOut</a> &amp;&#160;</td>
          <td class="paramname"><em>output</em></td><td>)</td>
          <td></td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">protected</span><span class="mlabel">virtual</span></span>  </td>
  </tr>
</table>
</div><div class="memdoc">

<p>Detect the SIFT keypoints for a set of points given in setInputCloud () using the spatial locator in setSearchMethod (). </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramname">output</td><td>the resultant cloud of keypoints </td></tr>
  </table>
  </dd>
</dl>

<p>实现了 <a class="el" href="classpcl_1_1_keypoint.html#abbe80461ac9e3bb7e355b4608d0b4674">pcl::Keypoint&lt; PointInT, PointOutT &gt;</a>.</p>
<div class="fragment"><div class="line"><a name="l00099"></a><span class="lineno">   99</span>&#160;{</div>
<div class="line"><a name="l00100"></a><span class="lineno">  100</span>&#160;  <span class="keywordflow">if</span> (<a class="code" href="classpcl_1_1_keypoint.html#a1998c46514f3f8d8d7665f6b505ca389">surface_</a> &amp;&amp; <a class="code" href="classpcl_1_1_keypoint.html#a1998c46514f3f8d8d7665f6b505ca389">surface_</a> != <a class="code" href="classpcl_1_1_p_c_l_base.html#a09c70d8e06e3fb4f07903fe6f8d67869">input_</a>)</div>
<div class="line"><a name="l00101"></a><span class="lineno">  101</span>&#160;  {</div>
<div class="line"><a name="l00102"></a><span class="lineno">  102</span>&#160;    PCL_WARN (<span class="stringliteral">&quot;[pcl::%s::detectKeypoints] : &quot;</span>, <a class="code" href="classpcl_1_1_keypoint.html#aef5710b81d66e6d429a9dfeb41d606ff">name_</a>.c_str ());</div>
<div class="line"><a name="l00103"></a><span class="lineno">  103</span>&#160;    PCL_WARN (<span class="stringliteral">&quot;A search surface has been set by setSearchSurface, but this SIFT keypoint detection algorithm does &quot;</span>);</div>
<div class="line"><a name="l00104"></a><span class="lineno">  104</span>&#160;    PCL_WARN (<span class="stringliteral">&quot;not support search surfaces other than the input cloud.  &quot;</span>);</div>
<div class="line"><a name="l00105"></a><span class="lineno">  105</span>&#160;    PCL_WARN (<span class="stringliteral">&quot;The cloud provided in setInputCloud is being used instead.\n&quot;</span>);</div>
<div class="line"><a name="l00106"></a><span class="lineno">  106</span>&#160;  }</div>
<div class="line"><a name="l00107"></a><span class="lineno">  107</span>&#160; </div>
<div class="line"><a name="l00108"></a><span class="lineno">  108</span>&#160;  <span class="comment">// Check if the output has a &quot;scale&quot; field</span></div>
<div class="line"><a name="l00109"></a><span class="lineno">  109</span>&#160;  <a class="code" href="classpcl_1_1_s_i_f_t_keypoint.html#a49af88108a4e7a9e769185be0c5dc84a">scale_idx_</a> = pcl::getFieldIndex&lt;PointOutT&gt; (output, <span class="stringliteral">&quot;scale&quot;</span>, <a class="code" href="classpcl_1_1_s_i_f_t_keypoint.html#abe6d16ca2a452c065d83f32f7a9416d6">out_fields_</a>);</div>
<div class="line"><a name="l00110"></a><span class="lineno">  110</span>&#160; </div>
<div class="line"><a name="l00111"></a><span class="lineno">  111</span>&#160;  <span class="comment">// Make sure the output cloud is empty</span></div>
<div class="line"><a name="l00112"></a><span class="lineno">  112</span>&#160;  output.points.clear ();</div>
<div class="line"><a name="l00113"></a><span class="lineno">  113</span>&#160; </div>
<div class="line"><a name="l00114"></a><span class="lineno">  114</span>&#160;  <span class="comment">// Create a local copy of the input cloud that will be resized for each octave</span></div>
<div class="line"><a name="l00115"></a><span class="lineno">  115</span>&#160;  boost::shared_ptr&lt;pcl::PointCloud&lt;PointInT&gt; &gt; cloud (<span class="keyword">new</span> <a class="code" href="classpcl_1_1_point_cloud.html">pcl::PointCloud&lt;PointInT&gt;</a> (*<a class="code" href="classpcl_1_1_p_c_l_base.html#a09c70d8e06e3fb4f07903fe6f8d67869">input_</a>));</div>
<div class="line"><a name="l00116"></a><span class="lineno">  116</span>&#160; </div>
<div class="line"><a name="l00117"></a><span class="lineno">  117</span>&#160;  VoxelGrid&lt;PointInT&gt; voxel_grid;</div>
<div class="line"><a name="l00118"></a><span class="lineno">  118</span>&#160;  <span class="comment">// Search for keypoints at each octave</span></div>
<div class="line"><a name="l00119"></a><span class="lineno">  119</span>&#160;  <span class="keywordtype">float</span> scale = <a class="code" href="classpcl_1_1_s_i_f_t_keypoint.html#a79b4f1c9356c27051d7ec7a2c471b991">min_scale_</a>;</div>
<div class="line"><a name="l00120"></a><span class="lineno">  120</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i_octave = 0; i_octave &lt; <a class="code" href="classpcl_1_1_s_i_f_t_keypoint.html#ab907e9fe76da88d568ab5d1c8151d6d9">nr_octaves_</a>; ++i_octave)</div>
<div class="line"><a name="l00121"></a><span class="lineno">  121</span>&#160;  {</div>
<div class="line"><a name="l00122"></a><span class="lineno">  122</span>&#160;    <span class="comment">// Downsample the point cloud</span></div>
<div class="line"><a name="l00123"></a><span class="lineno">  123</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">float</span> s = 1.0f * scale; <span class="comment">// note: this can be adjusted</span></div>
<div class="line"><a name="l00124"></a><span class="lineno">  124</span>&#160;    voxel_grid.setLeafSize (s, s, s);</div>
<div class="line"><a name="l00125"></a><span class="lineno">  125</span>&#160;    voxel_grid.setInputCloud (cloud);</div>
<div class="line"><a name="l00126"></a><span class="lineno">  126</span>&#160;    boost::shared_ptr&lt;pcl::PointCloud&lt;PointInT&gt; &gt; temp (<span class="keyword">new</span> <a class="code" href="classpcl_1_1_point_cloud.html">pcl::PointCloud&lt;PointInT&gt;</a>);    </div>
<div class="line"><a name="l00127"></a><span class="lineno">  127</span>&#160;    voxel_grid.filter (*temp);</div>
<div class="line"><a name="l00128"></a><span class="lineno">  128</span>&#160;    cloud = temp;</div>
<div class="line"><a name="l00129"></a><span class="lineno">  129</span>&#160; </div>
<div class="line"><a name="l00130"></a><span class="lineno">  130</span>&#160;    <span class="comment">// Make sure the downsampled cloud still has enough points</span></div>
<div class="line"><a name="l00131"></a><span class="lineno">  131</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">size_t</span> min_nr_points = 25;</div>
<div class="line"><a name="l00132"></a><span class="lineno">  132</span>&#160;    <span class="keywordflow">if</span> (cloud-&gt;points.size () &lt; min_nr_points)</div>
<div class="line"><a name="l00133"></a><span class="lineno">  133</span>&#160;      <span class="keywordflow">break</span>;</div>
<div class="line"><a name="l00134"></a><span class="lineno">  134</span>&#160; </div>
<div class="line"><a name="l00135"></a><span class="lineno">  135</span>&#160;    <span class="comment">// Update the KdTree with the downsampled points</span></div>
<div class="line"><a name="l00136"></a><span class="lineno">  136</span>&#160;    <a class="code" href="classpcl_1_1_keypoint.html#a9bca452f3813c88d994dba18d4a7a1c8">tree_</a>-&gt;setInputCloud (cloud);</div>
<div class="line"><a name="l00137"></a><span class="lineno">  137</span>&#160; </div>
<div class="line"><a name="l00138"></a><span class="lineno">  138</span>&#160;    <span class="comment">// Detect keypoints for the current scale</span></div>
<div class="line"><a name="l00139"></a><span class="lineno">  139</span>&#160;    <a class="code" href="classpcl_1_1_s_i_f_t_keypoint.html#a2b424cc026e8d0a693697a22eb360cfc">detectKeypointsForOctave</a> (*cloud, *<a class="code" href="classpcl_1_1_keypoint.html#a9bca452f3813c88d994dba18d4a7a1c8">tree_</a>, scale, <a class="code" href="classpcl_1_1_s_i_f_t_keypoint.html#a259a9b80428da022653896a1c16372cc">nr_scales_per_octave_</a>, output);</div>
<div class="line"><a name="l00140"></a><span class="lineno">  140</span>&#160; </div>
<div class="line"><a name="l00141"></a><span class="lineno">  141</span>&#160;    <span class="comment">// Increase the scale by another octave</span></div>
<div class="line"><a name="l00142"></a><span class="lineno">  142</span>&#160;    scale *= 2;</div>
<div class="line"><a name="l00143"></a><span class="lineno">  143</span>&#160;  }</div>
<div class="line"><a name="l00144"></a><span class="lineno">  144</span>&#160; </div>
<div class="line"><a name="l00145"></a><span class="lineno">  145</span>&#160;  <span class="comment">// Set final properties</span></div>
<div class="line"><a name="l00146"></a><span class="lineno">  146</span>&#160;  output.height = 1;</div>
<div class="line"><a name="l00147"></a><span class="lineno">  147</span>&#160;  output.width = <span class="keyword">static_cast&lt;</span>uint32_t<span class="keyword">&gt;</span> (output.points.size ());</div>
<div class="line"><a name="l00148"></a><span class="lineno">  148</span>&#160;  output.header = <a class="code" href="classpcl_1_1_p_c_l_base.html#a09c70d8e06e3fb4f07903fe6f8d67869">input_</a>-&gt;header;</div>
<div class="line"><a name="l00149"></a><span class="lineno">  149</span>&#160;  output.sensor_origin_ = <a class="code" href="classpcl_1_1_p_c_l_base.html#a09c70d8e06e3fb4f07903fe6f8d67869">input_</a>-&gt;sensor_origin_;</div>
<div class="line"><a name="l00150"></a><span class="lineno">  150</span>&#160;  output.sensor_orientation_ = <a class="code" href="classpcl_1_1_p_c_l_base.html#a09c70d8e06e3fb4f07903fe6f8d67869">input_</a>-&gt;sensor_orientation_;</div>
<div class="line"><a name="l00151"></a><span class="lineno">  151</span>&#160;}</div>
<div class="ttc" id="aclasspcl_1_1_keypoint_html_a1998c46514f3f8d8d7665f6b505ca389"><div class="ttname"><a href="classpcl_1_1_keypoint.html#a1998c46514f3f8d8d7665f6b505ca389">pcl::Keypoint::surface_</a></div><div class="ttdeci">PointCloudInConstPtr surface_</div><div class="ttdoc">An input point cloud describing the surface that is to be used for nearest neighbors estimation.</div><div class="ttdef"><b>Definition:</b> keypoint.h:182</div></div>
<div class="ttc" id="aclasspcl_1_1_keypoint_html_a9bca452f3813c88d994dba18d4a7a1c8"><div class="ttname"><a href="classpcl_1_1_keypoint.html#a9bca452f3813c88d994dba18d4a7a1c8">pcl::Keypoint::tree_</a></div><div class="ttdeci">KdTreePtr tree_</div><div class="ttdoc">A pointer to the spatial search object.</div><div class="ttdef"><b>Definition:</b> keypoint.h:185</div></div>
<div class="ttc" id="aclasspcl_1_1_keypoint_html_aef5710b81d66e6d429a9dfeb41d606ff"><div class="ttname"><a href="classpcl_1_1_keypoint.html#aef5710b81d66e6d429a9dfeb41d606ff">pcl::Keypoint::name_</a></div><div class="ttdeci">std::string name_</div><div class="ttdoc">The key point detection method's name.</div><div class="ttdef"><b>Definition:</b> keypoint.h:173</div></div>
<div class="ttc" id="aclasspcl_1_1_p_c_l_base_html_a09c70d8e06e3fb4f07903fe6f8d67869"><div class="ttname"><a href="classpcl_1_1_p_c_l_base.html#a09c70d8e06e3fb4f07903fe6f8d67869">pcl::PCLBase&lt; PointInT &gt;::input_</a></div><div class="ttdeci">PointCloudConstPtr input_</div><div class="ttdoc">The input point cloud dataset.</div><div class="ttdef"><b>Definition:</b> pcl_base.h:150</div></div>
<div class="ttc" id="aclasspcl_1_1_point_cloud_html"><div class="ttname"><a href="classpcl_1_1_point_cloud.html">pcl::PointCloud&lt; PointInT &gt;</a></div></div>
<div class="ttc" id="aclasspcl_1_1_s_i_f_t_keypoint_html_a259a9b80428da022653896a1c16372cc"><div class="ttname"><a href="classpcl_1_1_s_i_f_t_keypoint.html#a259a9b80428da022653896a1c16372cc">pcl::SIFTKeypoint::nr_scales_per_octave_</a></div><div class="ttdeci">int nr_scales_per_octave_</div><div class="ttdoc">The number of scales to be computed for each octave.</div><div class="ttdef"><b>Definition:</b> sift_keypoint.h:188</div></div>
<div class="ttc" id="aclasspcl_1_1_s_i_f_t_keypoint_html_a2b424cc026e8d0a693697a22eb360cfc"><div class="ttname"><a href="classpcl_1_1_s_i_f_t_keypoint.html#a2b424cc026e8d0a693697a22eb360cfc">pcl::SIFTKeypoint::detectKeypointsForOctave</a></div><div class="ttdeci">void detectKeypointsForOctave(const PointCloudIn &amp;input, KdTree &amp;tree, float base_scale, int nr_scales_per_octave, PointCloudOut &amp;output)</div><div class="ttdoc">Detect the SIFT keypoints for a given point cloud for a single octave.</div><div class="ttdef"><b>Definition:</b> sift_keypoint.hpp:156</div></div>
<div class="ttc" id="aclasspcl_1_1_s_i_f_t_keypoint_html_a49af88108a4e7a9e769185be0c5dc84a"><div class="ttname"><a href="classpcl_1_1_s_i_f_t_keypoint.html#a49af88108a4e7a9e769185be0c5dc84a">pcl::SIFTKeypoint::scale_idx_</a></div><div class="ttdeci">int scale_idx_</div><div class="ttdoc">Set to a value different than -1 if the output cloud has a &quot;scale&quot; field and we have to save the keyp...</div><div class="ttdef"><b>Definition:</b> sift_keypoint.h:195</div></div>
<div class="ttc" id="aclasspcl_1_1_s_i_f_t_keypoint_html_a79b4f1c9356c27051d7ec7a2c471b991"><div class="ttname"><a href="classpcl_1_1_s_i_f_t_keypoint.html#a79b4f1c9356c27051d7ec7a2c471b991">pcl::SIFTKeypoint::min_scale_</a></div><div class="ttdeci">float min_scale_</div><div class="ttdoc">The standard deviation of the smallest scale in the scale space.</div><div class="ttdef"><b>Definition:</b> sift_keypoint.h:182</div></div>
<div class="ttc" id="aclasspcl_1_1_s_i_f_t_keypoint_html_ab907e9fe76da88d568ab5d1c8151d6d9"><div class="ttname"><a href="classpcl_1_1_s_i_f_t_keypoint.html#ab907e9fe76da88d568ab5d1c8151d6d9">pcl::SIFTKeypoint::nr_octaves_</a></div><div class="ttdeci">int nr_octaves_</div><div class="ttdoc">The number of octaves (i.e. doublings of scale) over which to search for keypoints.</div><div class="ttdef"><b>Definition:</b> sift_keypoint.h:185</div></div>
<div class="ttc" id="aclasspcl_1_1_s_i_f_t_keypoint_html_abe6d16ca2a452c065d83f32f7a9416d6"><div class="ttname"><a href="classpcl_1_1_s_i_f_t_keypoint.html#abe6d16ca2a452c065d83f32f7a9416d6">pcl::SIFTKeypoint::out_fields_</a></div><div class="ttdeci">std::vector&lt; pcl::PCLPointField &gt; out_fields_</div><div class="ttdoc">The list of fields present in the output point cloud data.</div><div class="ttdef"><b>Definition:</b> sift_keypoint.h:198</div></div>
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<h2 class="memtitle"><span class="permalink"><a href="#a2b424cc026e8d0a693697a22eb360cfc">&#9670;&nbsp;</a></span>detectKeypointsForOctave()</h2>

<div class="memitem">
<div class="memproto">
<div class="memtemplate">
template&lt;typename PointInT , typename PointOutT &gt; </div>
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">void <a class="el" href="classpcl_1_1_s_i_f_t_keypoint.html">pcl::SIFTKeypoint</a>&lt; PointInT, PointOutT &gt;::detectKeypointsForOctave </td>
          <td>(</td>
          <td class="paramtype">const <a class="el" href="classpcl_1_1_point_cloud.html">PointCloudIn</a> &amp;&#160;</td>
          <td class="paramname"><em>input</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype"><a class="el" href="classpcl_1_1search_1_1_search.html">KdTree</a> &amp;&#160;</td>
          <td class="paramname"><em>tree</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">float&#160;</td>
          <td class="paramname"><em>base_scale</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">int&#160;</td>
          <td class="paramname"><em>nr_scales_per_octave</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype"><a class="el" href="classpcl_1_1_point_cloud.html">PointCloudOut</a> &amp;&#160;</td>
          <td class="paramname"><em>output</em>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">private</span></span>  </td>
  </tr>
</table>
</div><div class="memdoc">

<p>Detect the SIFT keypoints for a given point cloud for a single octave. </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramname">input</td><td>the point cloud to detect keypoints in </td></tr>
    <tr><td class="paramname">tree</td><td>a k-D tree of the points in <em>input</em> </td></tr>
    <tr><td class="paramname">base_scale</td><td>the first (smallest) scale in the octave </td></tr>
    <tr><td class="paramname">nr_scales_per_octave</td><td>the number of scales to to compute </td></tr>
    <tr><td class="paramname">output</td><td>the resultant point cloud containing the SIFT keypoints </td></tr>
  </table>
  </dd>
</dl>
<div class="fragment"><div class="line"><a name="l00159"></a><span class="lineno">  159</span>&#160;{</div>
<div class="line"><a name="l00160"></a><span class="lineno">  160</span>&#160;  <span class="comment">// Compute the difference of Gaussians (DoG) scale space</span></div>
<div class="line"><a name="l00161"></a><span class="lineno">  161</span>&#160;  std::vector&lt;float&gt; scales (nr_scales_per_octave + 3);</div>
<div class="line"><a name="l00162"></a><span class="lineno">  162</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i_scale = 0; i_scale &lt;= nr_scales_per_octave + 2; ++i_scale)</div>
<div class="line"><a name="l00163"></a><span class="lineno">  163</span>&#160;  {</div>
<div class="line"><a name="l00164"></a><span class="lineno">  164</span>&#160;    scales[i_scale] = base_scale * powf (2.0f, (1.0f * <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span> (i_scale) - 1.0f) / <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span> (nr_scales_per_octave));</div>
<div class="line"><a name="l00165"></a><span class="lineno">  165</span>&#160;  }</div>
<div class="line"><a name="l00166"></a><span class="lineno">  166</span>&#160;  Eigen::MatrixXf diff_of_gauss;</div>
<div class="line"><a name="l00167"></a><span class="lineno">  167</span>&#160;  <a class="code" href="classpcl_1_1_s_i_f_t_keypoint.html#a7b381aa10dc8c1701e7f1764a620bb8a">computeScaleSpace</a> (input, tree, scales, diff_of_gauss);</div>
<div class="line"><a name="l00168"></a><span class="lineno">  168</span>&#160; </div>
<div class="line"><a name="l00169"></a><span class="lineno">  169</span>&#160;  <span class="comment">// Find extrema in the DoG scale space</span></div>
<div class="line"><a name="l00170"></a><span class="lineno">  170</span>&#160;  std::vector&lt;int&gt; extrema_indices, extrema_scales;</div>
<div class="line"><a name="l00171"></a><span class="lineno">  171</span>&#160;  <a class="code" href="classpcl_1_1_s_i_f_t_keypoint.html#a2523f0256ba669b659a039da1b18a95c">findScaleSpaceExtrema</a> (input, tree, diff_of_gauss, extrema_indices, extrema_scales);</div>
<div class="line"><a name="l00172"></a><span class="lineno">  172</span>&#160; </div>
<div class="line"><a name="l00173"></a><span class="lineno">  173</span>&#160;  output.points.reserve (output.points.size () + extrema_indices.size ());</div>
<div class="line"><a name="l00174"></a><span class="lineno">  174</span>&#160;  <span class="comment">// Save scale?</span></div>
<div class="line"><a name="l00175"></a><span class="lineno">  175</span>&#160;  <span class="keywordflow">if</span> (<a class="code" href="classpcl_1_1_s_i_f_t_keypoint.html#a49af88108a4e7a9e769185be0c5dc84a">scale_idx_</a> != -1)</div>
<div class="line"><a name="l00176"></a><span class="lineno">  176</span>&#160;  {</div>
<div class="line"><a name="l00177"></a><span class="lineno">  177</span>&#160;    <span class="comment">// Add keypoints to output</span></div>
<div class="line"><a name="l00178"></a><span class="lineno">  178</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i_keypoint = 0; i_keypoint &lt; extrema_indices.size (); ++i_keypoint)</div>
<div class="line"><a name="l00179"></a><span class="lineno">  179</span>&#160;    {</div>
<div class="line"><a name="l00180"></a><span class="lineno">  180</span>&#160;      PointOutT keypoint;</div>
<div class="line"><a name="l00181"></a><span class="lineno">  181</span>&#160;      <span class="keyword">const</span> <span class="keywordtype">int</span> &amp;keypoint_index = extrema_indices[i_keypoint];</div>
<div class="line"><a name="l00182"></a><span class="lineno">  182</span>&#160;   </div>
<div class="line"><a name="l00183"></a><span class="lineno">  183</span>&#160;      keypoint.x = input.points[keypoint_index].x;</div>
<div class="line"><a name="l00184"></a><span class="lineno">  184</span>&#160;      keypoint.y = input.points[keypoint_index].y;</div>
<div class="line"><a name="l00185"></a><span class="lineno">  185</span>&#160;      keypoint.z = input.points[keypoint_index].z;</div>
<div class="line"><a name="l00186"></a><span class="lineno">  186</span>&#160;      memcpy (<span class="keyword">reinterpret_cast&lt;</span><span class="keywordtype">char</span>*<span class="keyword">&gt;</span> (&amp;keypoint) + <a class="code" href="classpcl_1_1_s_i_f_t_keypoint.html#abe6d16ca2a452c065d83f32f7a9416d6">out_fields_</a>[<a class="code" href="classpcl_1_1_s_i_f_t_keypoint.html#a49af88108a4e7a9e769185be0c5dc84a">scale_idx_</a>].offset,</div>
<div class="line"><a name="l00187"></a><span class="lineno">  187</span>&#160;              &amp;scales[extrema_scales[i_keypoint]], <span class="keyword">sizeof</span> (<span class="keywordtype">float</span>));</div>
<div class="line"><a name="l00188"></a><span class="lineno">  188</span>&#160;      output.points.push_back (keypoint); </div>
<div class="line"><a name="l00189"></a><span class="lineno">  189</span>&#160;    }</div>
<div class="line"><a name="l00190"></a><span class="lineno">  190</span>&#160;  }</div>
<div class="line"><a name="l00191"></a><span class="lineno">  191</span>&#160;  <span class="keywordflow">else</span></div>
<div class="line"><a name="l00192"></a><span class="lineno">  192</span>&#160;  {</div>
<div class="line"><a name="l00193"></a><span class="lineno">  193</span>&#160;    <span class="comment">// Add keypoints to output</span></div>
<div class="line"><a name="l00194"></a><span class="lineno">  194</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i_keypoint = 0; i_keypoint &lt; extrema_indices.size (); ++i_keypoint)</div>
<div class="line"><a name="l00195"></a><span class="lineno">  195</span>&#160;    {</div>
<div class="line"><a name="l00196"></a><span class="lineno">  196</span>&#160;      PointOutT keypoint;</div>
<div class="line"><a name="l00197"></a><span class="lineno">  197</span>&#160;      <span class="keyword">const</span> <span class="keywordtype">int</span> &amp;keypoint_index = extrema_indices[i_keypoint];</div>
<div class="line"><a name="l00198"></a><span class="lineno">  198</span>&#160;   </div>
<div class="line"><a name="l00199"></a><span class="lineno">  199</span>&#160;      keypoint.x = input.points[keypoint_index].x;</div>
<div class="line"><a name="l00200"></a><span class="lineno">  200</span>&#160;      keypoint.y = input.points[keypoint_index].y;</div>
<div class="line"><a name="l00201"></a><span class="lineno">  201</span>&#160;      keypoint.z = input.points[keypoint_index].z;</div>
<div class="line"><a name="l00202"></a><span class="lineno">  202</span>&#160; </div>
<div class="line"><a name="l00203"></a><span class="lineno">  203</span>&#160;      output.points.push_back (keypoint); </div>
<div class="line"><a name="l00204"></a><span class="lineno">  204</span>&#160;    }</div>
<div class="line"><a name="l00205"></a><span class="lineno">  205</span>&#160;  }</div>
<div class="line"><a name="l00206"></a><span class="lineno">  206</span>&#160;}</div>
<div class="ttc" id="aclasspcl_1_1_s_i_f_t_keypoint_html_a2523f0256ba669b659a039da1b18a95c"><div class="ttname"><a href="classpcl_1_1_s_i_f_t_keypoint.html#a2523f0256ba669b659a039da1b18a95c">pcl::SIFTKeypoint::findScaleSpaceExtrema</a></div><div class="ttdeci">void findScaleSpaceExtrema(const PointCloudIn &amp;input, KdTree &amp;tree, const Eigen::MatrixXf &amp;diff_of_gauss, std::vector&lt; int &gt; &amp;extrema_indices, std::vector&lt; int &gt; &amp;extrema_scales)</div><div class="ttdoc">Find the local minima and maxima in the provided difference-of-Gaussian (DoG) scale space</div><div class="ttdef"><b>Definition:</b> sift_keypoint.hpp:262</div></div>
<div class="ttc" id="aclasspcl_1_1_s_i_f_t_keypoint_html_a7b381aa10dc8c1701e7f1764a620bb8a"><div class="ttname"><a href="classpcl_1_1_s_i_f_t_keypoint.html#a7b381aa10dc8c1701e7f1764a620bb8a">pcl::SIFTKeypoint::computeScaleSpace</a></div><div class="ttdeci">void computeScaleSpace(const PointCloudIn &amp;input, KdTree &amp;tree, const std::vector&lt; float &gt; &amp;scales, Eigen::MatrixXf &amp;diff_of_gauss)</div><div class="ttdoc">Compute the difference-of-Gaussian (DoG) scale space for the given input and scales</div><div class="ttdef"><b>Definition:</b> sift_keypoint.hpp:211</div></div>
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<h2 class="memtitle"><span class="permalink"><a href="#a2523f0256ba669b659a039da1b18a95c">&#9670;&nbsp;</a></span>findScaleSpaceExtrema()</h2>

<div class="memitem">
<div class="memproto">
<div class="memtemplate">
template&lt;typename PointInT , typename PointOutT &gt; </div>
<table class="mlabels">
  <tr>
  <td class="mlabels-left">
      <table class="memname">
        <tr>
          <td class="memname">void <a class="el" href="classpcl_1_1_s_i_f_t_keypoint.html">pcl::SIFTKeypoint</a>&lt; PointInT, PointOutT &gt;::findScaleSpaceExtrema </td>
          <td>(</td>
          <td class="paramtype">const <a class="el" href="classpcl_1_1_point_cloud.html">PointCloudIn</a> &amp;&#160;</td>
          <td class="paramname"><em>input</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype"><a class="el" href="classpcl_1_1search_1_1_search.html">KdTree</a> &amp;&#160;</td>
          <td class="paramname"><em>tree</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const Eigen::MatrixXf &amp;&#160;</td>
          <td class="paramname"><em>diff_of_gauss</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">std::vector&lt; int &gt; &amp;&#160;</td>
          <td class="paramname"><em>extrema_indices</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">std::vector&lt; int &gt; &amp;&#160;</td>
          <td class="paramname"><em>extrema_scales</em>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
<span class="mlabels"><span class="mlabel">private</span></span>  </td>
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</table>
</div><div class="memdoc">

<p>Find the local minima and maxima in the provided difference-of-Gaussian (DoG) scale space </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramname">input</td><td>the input point cloud </td></tr>
    <tr><td class="paramname">tree</td><td>a k-D tree of the points in <em>input</em> </td></tr>
    <tr><td class="paramname">diff_of_gauss</td><td>the DoG scale space (in a number-of-points by number-of-scales matrix) </td></tr>
    <tr><td class="paramname">extrema_indices</td><td>the resultant vector containing the point indices of each keypoint </td></tr>
    <tr><td class="paramname">extrema_scales</td><td>the resultant vector containing the scale indices of each keypoint </td></tr>
  </table>
  </dd>
</dl>
<div class="fragment"><div class="line"><a name="l00265"></a><span class="lineno">  265</span>&#160;{</div>
<div class="line"><a name="l00266"></a><span class="lineno">  266</span>&#160;  <span class="keyword">const</span> <span class="keywordtype">int</span> k = 25;</div>
<div class="line"><a name="l00267"></a><span class="lineno">  267</span>&#160;  std::vector&lt;int&gt; nn_indices (k);</div>
<div class="line"><a name="l00268"></a><span class="lineno">  268</span>&#160;  std::vector&lt;float&gt; nn_dist (k);</div>
<div class="line"><a name="l00269"></a><span class="lineno">  269</span>&#160; </div>
<div class="line"><a name="l00270"></a><span class="lineno">  270</span>&#160;  <span class="keyword">const</span> <span class="keywordtype">int</span> nr_scales = <span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span> (diff_of_gauss.cols ());</div>
<div class="line"><a name="l00271"></a><span class="lineno">  271</span>&#160;  std::vector&lt;float&gt; min_val (nr_scales), max_val (nr_scales);</div>
<div class="line"><a name="l00272"></a><span class="lineno">  272</span>&#160; </div>
<div class="line"><a name="l00273"></a><span class="lineno">  273</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i_point = 0; i_point &lt; static_cast&lt;int&gt; (input.size ()); ++i_point)</div>
<div class="line"><a name="l00274"></a><span class="lineno">  274</span>&#160;  {</div>
<div class="line"><a name="l00275"></a><span class="lineno">  275</span>&#160;    <span class="comment">// Define the local neighborhood around the current point</span></div>
<div class="line"><a name="l00276"></a><span class="lineno">  276</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">size_t</span> nr_nn = tree.nearestKSearch (i_point, k, nn_indices, nn_dist); <span class="comment">//*</span></div>
<div class="line"><a name="l00277"></a><span class="lineno">  277</span>&#160;    <span class="comment">// * note: the neighborhood for finding local extrema is best defined as a small fixed-k neighborhood, regardless of</span></div>
<div class="line"><a name="l00278"></a><span class="lineno">  278</span>&#160;    <span class="comment">//   the configurable search method specified by the user, so we directly employ tree.nearestKSearch here instead </span></div>
<div class="line"><a name="l00279"></a><span class="lineno">  279</span>&#160;    <span class="comment">//   of using searchForNeighbors</span></div>
<div class="line"><a name="l00280"></a><span class="lineno">  280</span>&#160; </div>
<div class="line"><a name="l00281"></a><span class="lineno">  281</span>&#160;    <span class="comment">// At each scale, find the extreme values of the DoG within the current neighborhood</span></div>
<div class="line"><a name="l00282"></a><span class="lineno">  282</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i_scale = 0; i_scale &lt; nr_scales; ++i_scale)</div>
<div class="line"><a name="l00283"></a><span class="lineno">  283</span>&#160;    {</div>
<div class="line"><a name="l00284"></a><span class="lineno">  284</span>&#160;      min_val[i_scale] = std::numeric_limits&lt;float&gt;::max ();</div>
<div class="line"><a name="l00285"></a><span class="lineno">  285</span>&#160;      max_val[i_scale] = -std::numeric_limits&lt;float&gt;::max ();</div>
<div class="line"><a name="l00286"></a><span class="lineno">  286</span>&#160; </div>
<div class="line"><a name="l00287"></a><span class="lineno">  287</span>&#160;      <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i_neighbor = 0; i_neighbor &lt; nr_nn; ++i_neighbor)</div>
<div class="line"><a name="l00288"></a><span class="lineno">  288</span>&#160;      {</div>
<div class="line"><a name="l00289"></a><span class="lineno">  289</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">float</span> &amp;d = diff_of_gauss (nn_indices[i_neighbor], i_scale);</div>
<div class="line"><a name="l00290"></a><span class="lineno">  290</span>&#160; </div>
<div class="line"><a name="l00291"></a><span class="lineno">  291</span>&#160;        min_val[i_scale] = (std::min) (min_val[i_scale], d);</div>
<div class="line"><a name="l00292"></a><span class="lineno">  292</span>&#160;        max_val[i_scale] = (std::max) (max_val[i_scale], d);</div>
<div class="line"><a name="l00293"></a><span class="lineno">  293</span>&#160;      }</div>
<div class="line"><a name="l00294"></a><span class="lineno">  294</span>&#160;    }</div>
<div class="line"><a name="l00295"></a><span class="lineno">  295</span>&#160; </div>
<div class="line"><a name="l00296"></a><span class="lineno">  296</span>&#160;    <span class="comment">// If the current point is an extreme value with high enough contrast, add it as a keypoint </span></div>
<div class="line"><a name="l00297"></a><span class="lineno">  297</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i_scale = 1; i_scale &lt; nr_scales - 1; ++i_scale)</div>
<div class="line"><a name="l00298"></a><span class="lineno">  298</span>&#160;    {</div>
<div class="line"><a name="l00299"></a><span class="lineno">  299</span>&#160;      <span class="keyword">const</span> <span class="keywordtype">float</span> &amp;val = diff_of_gauss (i_point, i_scale);</div>
<div class="line"><a name="l00300"></a><span class="lineno">  300</span>&#160; </div>
<div class="line"><a name="l00301"></a><span class="lineno">  301</span>&#160;      <span class="comment">// Does the point have sufficient contrast?</span></div>
<div class="line"><a name="l00302"></a><span class="lineno">  302</span>&#160;      <span class="keywordflow">if</span> (fabs (val) &gt;= <a class="code" href="classpcl_1_1_s_i_f_t_keypoint.html#ac982d9e5cb07edb578b262e588c08841">min_contrast_</a>)</div>
<div class="line"><a name="l00303"></a><span class="lineno">  303</span>&#160;      {</div>
<div class="line"><a name="l00304"></a><span class="lineno">  304</span>&#160;        <span class="comment">// Is it a local minimum?</span></div>
<div class="line"><a name="l00305"></a><span class="lineno">  305</span>&#160;        <span class="keywordflow">if</span> ((val == min_val[i_scale]) &amp;&amp; </div>
<div class="line"><a name="l00306"></a><span class="lineno">  306</span>&#160;            (val &lt;  min_val[i_scale - 1]) &amp;&amp; </div>
<div class="line"><a name="l00307"></a><span class="lineno">  307</span>&#160;            (val &lt;  min_val[i_scale + 1]))</div>
<div class="line"><a name="l00308"></a><span class="lineno">  308</span>&#160;        {</div>
<div class="line"><a name="l00309"></a><span class="lineno">  309</span>&#160;          extrema_indices.push_back (i_point);</div>
<div class="line"><a name="l00310"></a><span class="lineno">  310</span>&#160;          extrema_scales.push_back (i_scale);</div>
<div class="line"><a name="l00311"></a><span class="lineno">  311</span>&#160;        }</div>
<div class="line"><a name="l00312"></a><span class="lineno">  312</span>&#160;        <span class="comment">// Is it a local maximum?</span></div>
<div class="line"><a name="l00313"></a><span class="lineno">  313</span>&#160;        <span class="keywordflow">else</span> <span class="keywordflow">if</span> ((val == max_val[i_scale]) &amp;&amp; </div>
<div class="line"><a name="l00314"></a><span class="lineno">  314</span>&#160;                 (val &gt;  max_val[i_scale - 1]) &amp;&amp; </div>
<div class="line"><a name="l00315"></a><span class="lineno">  315</span>&#160;                 (val &gt;  max_val[i_scale + 1]))</div>
<div class="line"><a name="l00316"></a><span class="lineno">  316</span>&#160;        {</div>
<div class="line"><a name="l00317"></a><span class="lineno">  317</span>&#160;          extrema_indices.push_back (i_point);</div>
<div class="line"><a name="l00318"></a><span class="lineno">  318</span>&#160;          extrema_scales.push_back (i_scale);</div>
<div class="line"><a name="l00319"></a><span class="lineno">  319</span>&#160;        }</div>
<div class="line"><a name="l00320"></a><span class="lineno">  320</span>&#160;      }</div>
<div class="line"><a name="l00321"></a><span class="lineno">  321</span>&#160;    }</div>
<div class="line"><a name="l00322"></a><span class="lineno">  322</span>&#160;  }</div>
<div class="line"><a name="l00323"></a><span class="lineno">  323</span>&#160;}</div>
<div class="ttc" id="aclasspcl_1_1_s_i_f_t_keypoint_html_ac982d9e5cb07edb578b262e588c08841"><div class="ttname"><a href="classpcl_1_1_s_i_f_t_keypoint.html#ac982d9e5cb07edb578b262e588c08841">pcl::SIFTKeypoint::min_contrast_</a></div><div class="ttdeci">float min_contrast_</div><div class="ttdoc">The minimum contrast required for detection.</div><div class="ttdef"><b>Definition:</b> sift_keypoint.h:191</div></div>
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<h2 class="memtitle"><span class="permalink"><a href="#a5fcabfa4b90cc77cf0c18a5b263d06a0">&#9670;&nbsp;</a></span>setMinimumContrast()</h2>

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<div class="memproto">
<div class="memtemplate">
template&lt;typename PointInT , typename PointOutT &gt; </div>
      <table class="memname">
        <tr>
          <td class="memname">void <a class="el" href="classpcl_1_1_s_i_f_t_keypoint.html">pcl::SIFTKeypoint</a>&lt; PointInT, PointOutT &gt;::setMinimumContrast </td>
          <td>(</td>
          <td class="paramtype">float&#160;</td>
          <td class="paramname"><em>min_contrast</em></td><td>)</td>
          <td></td>
        </tr>
      </table>
</div><div class="memdoc">

<p>Provide a threshold to limit detection of keypoints without sufficient contrast </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramname">min_contrast</td><td>the minimum contrast required for detection </td></tr>
  </table>
  </dd>
</dl>
<div class="fragment"><div class="line"><a name="l00058"></a><span class="lineno">   58</span>&#160;{</div>
<div class="line"><a name="l00059"></a><span class="lineno">   59</span>&#160;  <a class="code" href="classpcl_1_1_s_i_f_t_keypoint.html#ac982d9e5cb07edb578b262e588c08841">min_contrast_</a> = min_contrast;</div>
<div class="line"><a name="l00060"></a><span class="lineno">   60</span>&#160;}</div>
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<h2 class="memtitle"><span class="permalink"><a href="#aa7b165d782eca9c9d226504b84729439">&#9670;&nbsp;</a></span>setScales()</h2>

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<div class="memproto">
<div class="memtemplate">
template&lt;typename PointInT , typename PointOutT &gt; </div>
      <table class="memname">
        <tr>
          <td class="memname">void <a class="el" href="classpcl_1_1_s_i_f_t_keypoint.html">pcl::SIFTKeypoint</a>&lt; PointInT, PointOutT &gt;::setScales </td>
          <td>(</td>
          <td class="paramtype">float&#160;</td>
          <td class="paramname"><em>min_scale</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">int&#160;</td>
          <td class="paramname"><em>nr_octaves</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">int&#160;</td>
          <td class="paramname"><em>nr_scales_per_octave</em>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
        </tr>
      </table>
</div><div class="memdoc">

<p>Specify the range of scales over which to search for keypoints </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramname">min_scale</td><td>the standard deviation of the smallest scale in the scale space </td></tr>
    <tr><td class="paramname">nr_octaves</td><td>the number of octaves (i.e. doublings of scale) to compute </td></tr>
    <tr><td class="paramname">nr_scales_per_octave</td><td>the number of scales to compute within each octave </td></tr>
  </table>
  </dd>
</dl>
<div class="fragment"><div class="line"><a name="l00048"></a><span class="lineno">   48</span>&#160;{</div>
<div class="line"><a name="l00049"></a><span class="lineno">   49</span>&#160;  <a class="code" href="classpcl_1_1_s_i_f_t_keypoint.html#a79b4f1c9356c27051d7ec7a2c471b991">min_scale_</a> = min_scale;</div>
<div class="line"><a name="l00050"></a><span class="lineno">   50</span>&#160;  <a class="code" href="classpcl_1_1_s_i_f_t_keypoint.html#ab907e9fe76da88d568ab5d1c8151d6d9">nr_octaves_</a> = nr_octaves;</div>
<div class="line"><a name="l00051"></a><span class="lineno">   51</span>&#160;  <a class="code" href="classpcl_1_1_s_i_f_t_keypoint.html#a259a9b80428da022653896a1c16372cc">nr_scales_per_octave_</a> = nr_scales_per_octave;</div>
<div class="line"><a name="l00052"></a><span class="lineno">   52</span>&#160;}</div>
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<li>keypoints/include/pcl/keypoints/impl/<a class="el" href="sift__keypoint_8hpp_source.html">sift_keypoint.hpp</a></li>
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